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Westlake-AI/Awesome-Mixup

[Survey] Awesome List of Mixup Augmentation and Beyond (https://arxiv.org/abs/2409.05202)

awesome-listawesome-mixupcomputer-visiondata-augmentationdeep-learninggraphimage-classificationmixupnatural-language-processingself-supervised-learning

Awesome-Mixup

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Welcome to Awesome-Mixup, a carefully curated survey of Mixup algorithms implemented in the PyTorch library, aiming to meet various needs of the research community. Mixup is a kind of methods that focus on alleviating model overfitting and poor generalization. As a "data-centric" way, Mixup can be applied to various training paradigms and data modalities.

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Introduction

We summarize awesome mixup data augmentation methods for visual representation learning in various scenarios from 2018 to 2024.

The list of awesome mixup augmentation methods is summarized in chronological order and is on updating. The main branch is modified according to Awesome-Mixup in OpenMixup and Awesome-Mix, and we are working on a comperhensive survey on mixup augmentations. You can read our survey: A Survey on Mixup Augmentations and Beyond see more detailed information.

  • To find related papers and their relationships, check out Connected Papers, which visualizes the academic field in a graph representation.
  • To export BibTeX citations of papers, check out ArXiv or Semantic Scholar of the paper for professional reference formats.

Figuer of Contents

You can see the figuer of mixup augmentation methods deirtly that we summarized.

Table of Contents

Table of Contents
    Sample Mixup Policies in SL
    1. Static Linear
    2. Feature-based
    3. Cutting-based
    4. K Samples Mixup
    5. Random Policies
    6. Style-based
    7. Saliency-based
    8. Attention-based
    9. Generating Samples
    Label Mixup Policies in SL
    1. Optimizing Calibration
    2. Area-based
    3. Loss Object
    4. Random Label Policies
    5. Optimizing Mixing Ratio
    6. Generating Label
    7. Attention Score
    8. Saliency Token
    Self-Supervised Learning
    1. Contrastive Learning
    2. Masked Image Modeling
    Semi-Supervised Learning
    1. Semi-Supervised Learning
    CV Downstream Tasks
    1. Regression
    2. Long tail distribution
    3. Segmentation
    4. Object Detection
    Training Paradigms
    1. Federated Learning
    2. Adversarial Attack and Adversarial Training
    3. Domain Adaption
    4. Knowledge Distillation
    5. Multi Modal
    Beyond Vision
    1. NLP
    2. GNN
    3. 3D Point
    4. Other
  1. Analysis and Theorem
  2. Survey
  3. Benchmark
  4. Classification Results on Datasets
  5. Related Datasets Link
  6. Contribution
  7. License
  8. Acknowledgement
  9. Related Project

Sample Mixup Policies in SL

Static Linear

  • mixup: Beyond Empirical Risk Minimization

    Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz

    ICLR'2018 [Paper]
    [Code]

    MixUp Framework

  • Between-class Learning for Image Classification

    Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada

    CVPR'2018 [Paper]
    [Code]

    BC Framework

  • Preventing Manifold Intrusion with Locality: Local Mixup

    Raphael Baena, Lucas Drumetz, Vincent Gripon

    EUSIPCO'2022 [Paper]

    LocalMixup Framework

  • AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

    Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

    ICLR'2020 [Paper]
    [Code]

    AugMix Framework

  • DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness

    Ryuichiro Hataya, Hideki Nakayama

    arXiv'2021 [Paper]

    DJMix Framework

  • PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures

    Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt

    CVPR'2022 [Paper]
    [Code]

    PixMix Framework

  • IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers

    Zhenglin Huang, Xiaoan Bao, Na Zhang, Qingqi Zhang, Xiaomei Tu, Biao Wu, Xi Yang

    NIPS'2023 [Paper]
    [Code]

    IPMix Framework

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Feature-based

  • Manifold Mixup: Better Representations by Interpolating Hidden States

    Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio

    ICML'2019 [Paper]
    [Code]

    ManifoldMix Framework

  • PatchUp: A Regularization Technique for Convolutional Neural Networks

    Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan, Vikas Verma, Sarath Chandar

    arXiv'2020 [Paper]
    [Code]

    PatchUp Framework

  • On Feature Normalization and Data Augmentation

    Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Q. Weinberger

    CVPR'2021 [Paper]
    [Code]

    MoEx Framework

  • Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN

    Minsoo Kang, Minkoo Kang, Suhyun Kim

    AAAI'2024 [Paper]

    Catch-Up-Mix Framework

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Cutting-based

  • CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features

    Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo

    ICCV'2019 [Paper]
    [Code]

    CutMix Framework

  • Improved Mixed-Example Data Augmentation

    Cecilia Summers, Michael J. Dinneen

    WACV'2019 [Paper]
    [Code]

    MixedExamples Framework

  • Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy

    Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu

    arXiv'2019 [Paper]

    Pani VAT Framework

  • FMix: Enhancing Mixed Sample Data Augmentation

    Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon Hare

    arXiv'2020 [Paper]
    [Code]

    FMix Framework

  • SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers

    Jin-Ha Lee, Muhammad Zaigham Zaheer, Marcella Astrid, Seung-Ik Lee

    CVPRW'2020 [Paper]
    [Code]

    SmoothMix Framework

  • GridMix: Strong regularization through local context mapping

    Kyungjune Baek, Duhyeon Bang, Hyunjung Shim

    Pattern Recognition'2021 [Paper]
    [Code]

    GridMixup Framework

  • ResizeMix: Mixing Data with Preserved Object Information and True Labels

    Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, Xinggang Wang

    arXiv'2020 [Paper]
    [Code]

    ResizeMix Framework

  • StackMix: A complementary Mix algorithm

    John Chen, Samarth Sinha, Anastasios Kyrillidis

    UAI'2022 [Paper]

    StackMix Framework

  • SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation

    Karim Hammoudi, Adnane Cabani, Bouthaina Slika, Halim Benhabiles, Fadi Dornaika, Mahmoud Melkemi

    arXiv'2022 [Paper]
    [Code]

    SuperpixelGridCut Framework

  • A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective

    Chanwoo Park, Sangdoo Yun, Sanghyuk Chun

    NIPS'2022 [Paper]
    [Code]

    MSDA Framework

  • You Only Cut Once: Boosting Data Augmentation with a Single Cut

    Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian Reid, Lars Petersson, Hongdong Li

    ICML'2022 [Paper]
    [Code]

    YOCO Framework

  • StarLKNet: Star Mixup with Large Kernel Networks for Palm Vein Identification

    Xin Jin, Hongyu Zhu, Mounîm A.El Yacoubi, Hongchao Liao, Huafeng Qin, Yun Jiang

    arXiv'2024 [Paper]

    StarMix Framework

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K Samples Mixup

  • You Only Look Once: Unified, Real-Time Object Detection

    Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi

    CVPR'2016 [Paper]
    [Code]

    Mosaic

  • Data Augmentation using Random Image Cropping and Patching for Deep CNNs

    Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara

    IEEE TCSVT'2020 [Paper]

    RICAP

  • k-Mixup Regularization for Deep Learning via Optimal Transport

    Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien

    arXiv'2021 [Paper]

    k-Mixup Framework

  • Observations on K-image Expansion of Image-Mixing Augmentation for Classification

    Joonhyun Jeong, Sungmin Cha, Youngjoon Yoo, Sangdoo Yun, Taesup Moon, Jongwon Choi

    IEEE Access'2021 [Paper]
    [Code]

    DCutMix Framework

  • MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks

    Alexandre Rame, Remy Sun, Matthieu Cord

    ICCV'2021 [Paper]

    MixMo Framework

  • Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network

    Tianshu Xie, Xuan Cheng, Minghui Liu, Jiali Deng, Xiaomin Wang, Ming Liu

    ACM MM;2021 [Paper]

    Cut-Thumbnail

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Random Policies

  • RandomMix: A mixed sample data augmentation method with multiple mixed modes

    Xiaoliang Liu, Furao Shen, Jian Zhao, Changhai Nie

    arXiv'2022 [Paper]

    RandomMix Framework

  • AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance

    Xiaoliang Liu, Furao Shen, Jian Zhao, Changhai Nie

    ICME'2022 [Paper]

    AugRmixAT Framework

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Style-based

  • StyleMix: Separating Content and Style for Enhanced Data Augmentation

    Minui Hong, Jinwoo Choi, Gunhee Kim

    CVPR'2021 [Paper]
    [Code]

    StyleMix Framework

  • Domain Generalization with MixStyle

    Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang

    ICLR'2021 [Paper]
    [Code]

    MixStyle Framework

  • AlignMix: Improving representation by interpolating aligned features

    Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis

    CVPR'2022 [Paper]
    [Code]

    AlignMixup Framework

  • Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples

    Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis

    NIPS'2023 [Paper]
    MultiMix Framework

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Saliency-based

  • SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization

    A F M Shahab Uddin and Mst. Sirazam Monira and Wheemyung Shin and TaeChoong Chung and Sung-Ho Bae

    ICLR'2021 [Paper]
    [Code]

    SaliencyMix Framework

  • Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification

    Devesh Walawalkar, Zhiqiang Shen, Zechun Liu, Marios Savvides

    ICASSP'2020 [Paper]
    [Code]

    AttentiveMix Framework

  • SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data

    Shaoli Huang, Xinchao Wang, Dacheng Tao

    AAAI'2021 [Paper]
    [Code]

    SnapMix Framework

  • Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition

    Hao Li, Xiaopeng Zhang, Hongkai Xiong, Qi Tian

    VCIP'2020 [Paper]

    AttributeMix Framework

  • Where to Cut and Paste: Data Regularization with Selective Features

    Jiyeon Kim, Ik-Hee Shin, Jong-Ryul, Lee, Yong-Ju Lee

    ICTC'2020 [Paper]
    [Code]

    FocusMix Framework

  • PuzzleMix: Exploiting Saliency and Local Statistics for Optimal Mixup

    Jang-Hyun Kim, Wonho Choo, Hyun Oh Song

    ICML'2020 [Paper]
    [Code]

    PuzzleMix Framework

  • Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity

    Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song

    ICLR'2021 [Paper]
    [Code]

    Co-Mixup Framework

  • SuperMix: Supervising the Mixing Data Augmentation

    Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Nasser M. Nasrabadi

    CVPR'2021 [Paper]
    [Code]

    SuperMix Framework

  • AutoMix: Unveiling the Power of Mixup for Stronger Classifiers

    Zicheng Liu, Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, Stan Z. Li

    ECCV'2022 [Paper]
    [Code]

    AutoMix Framework

  • Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup

    Siyuan Li, Zicheng Liu, Di Wu, Zihan Liu, Stan Z. Li

    arXiv'2021 [Paper]
    [Code]

    SAMix Framework

  • RecursiveMix: Mixed Learning with History

    Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang

    NIPS'2022 [Paper]
    [Code]

    RecursiveMix Framework

  • TransformMix: Learning Transformation and Mixing Strategies for Sample-mixing Data Augmentation

    Tsz-Him Cheung, Dit-Yan Yeung

    OpenReview'2023 [Paper]

    TransformMix Framework

  • GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps

    Minsoo Kang, Suhyun Kim

    AAAI'2023 [Paper]
    [Code]

    GuidedMixup Framework

  • GradSalMix: Gradient Saliency-Based Mix for Image Data Augmentation

    Tao Hong, Ya Wang, Xingwu Sun, Fengzong Lian, Zhanhui Kang, Jinwen Ma

    ICME'2023 [Paper]

    GradSalMix Framework

  • LGCOAMix: Local and Global Context-and-Object-Part-Aware Superpixel-Based Data Augmentation for Deep Visual Recognition

    Fadi Dornaika, Danyang Sun

    TIP'2023 [Paper]
    [Code]

    LGCOAMix Framework

  • Adversarial AutoMixup

    Huafeng Qin, Xin Jin, Yun Jiang, Mounim A. El-Yacoubi, Xinbo Gao

    ICLR'2024 [Paper]
    [Code]

    AdAutoMix Framework

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Attention-based

  • TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers

    Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, Yu Liu

    ECCV'2022 [Paper]
    [Code]

    TokenMix Framework

  • TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers

    Hyeong Kyu Choi, Joonmyung Choi, Hyunwoo J. Kim

    NIPS'2022 [Paper]
    [Code]

    TokenMixup Framework

  • ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

    Thomas Stegmüller, Behzad Bozorgtabar, Antoine Spahr, Jean-Philippe Thiran

    WACV'2023 [Paper]

    ScoreMix Framework

  • MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer

    Qihao Zhao, Yangyu Huang, Wei Hu, Fan Zhang, Jun Liu

    ICLR'2023 [Paper]
    [Code]

    MixPro Framework

  • SMMix: Self-Motivated Image Mixing for Vision Transformers

    Mengzhao Chen, Mingbao Lin, ZhiHang Lin, Yuxin Zhang, Fei Chao, Rongrong Ji

    ICCV'2023 [Paper]
    [Code]

    SMMix Framework

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Generating Samples

  • Data Augmentation via Latent Space Interpolation for Image Classification

    *Xiaofeng Liu, Yang Zou, Lingsheng Kong, Zhihui Diao, Junliang Yan, Jun Wang, Site Li, Ping Jia, Jane You

    ICPR'2018 [Paper]

    AEE Framework

  • On Adversarial Mixup Resynthesis

    Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Christopher Pal

    NIPS'2019 [Paper]
    [Code]

    AMR Framework

  • AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning

    Jianchao Zhu, Liangliang Shi, Junchi Yan, Hongyuan Zha

    ECCV'2020 [Paper]

    AutoMix Framework

  • VarMixup: Exploiting the Latent Space for Robust Training and Inference

    Puneet Mangla, Vedant Singh, Shreyas Jayant Havaldar, Vineeth N Balasubramanian

    CVPRW'2021 [Paper]

    VarMixup Framework

  • DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models

    Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar

    CVPR'2024 [Paper]
    [Code]

    DiffuseMix Framework

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Label Mixup Policies in SL

Optimizing Calibration

  • Combining Ensembles and Data Augmentation can Harm your Calibration

    Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran

    ICLR'2021 [Paper]
    [Code]

    CAMix Framework

  • RankMixup: Ranking-Based Mixup Training for Network Calibration

    Jongyoun Noh, Hyekang Park, Junghyup Lee, Bumsub Ham

    ICCV'2023 [Paper]
    [Code]

    RankMixup Framework

  • SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness

    Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Doguk Kim, Jinwoo Shin

    NIPS'2021 [Paper]
    [Code]

    SmoothMixup Framework

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Area-based

  • TransMix: Attend to Mix for Vision Transformers

    Jie-Neng Chen, Shuyang Sun, Ju He, Philip Torr, Alan Yuille, Song Bai

    CVPR'2022 [Paper]
    [Code]

    TransMix Framework

  • Data Augmentation using Random Image Cropping and Patching for Deep CNNs

    Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara

    IEEE TCSVT'2020 [Paper]

    RICAP

  • RecursiveMix: Mixed Learning with History

    Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang

    NIPS'2022 [Paper]
    [Code]

    RecursiveMix Framework

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Loss Object

  • Harnessing Hard Mixed Samples with Decoupled Regularizer

    Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li

    NIPS'2023 [Paper]
    [Code]

    DecoupledMix Framework

  • MixupE: Understanding and Improving Mixup from Directional Derivative Perspective

    Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi

    UAI'2023 [Paper]
    [Code]

    MixupE Framework

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Random Label Policies

  • Mixup Without Hesitation

    Hao Yu, Huanyu Wang, Jianxin Wu

    ICIG'2022 [Paper]
    [Code]

  • RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness

    Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip H.S. Torr, Puneet K. Dokania

    NIPS'2022 [Paper]
    [Code]

    RegMixup Framework

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Optimizing Mixing Ratio

  • MixUp as Locally Linear Out-Of-Manifold Regularization

    Hongyu Guo, Yongyi Mao, Richong Zhang

    AAAI'2019 [Paper]

    AdaMixup Framework

  • RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness

    Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip H.S. Torr, Puneet K. Dokania

    NIPS'2022 [Paper]
    [Code]

    RegMixup Framework

  • Metamixup: Learning adaptive interpolation policy of mixup with metalearning

    Zhijun Mai, Guosheng Hu, Dexiong Chen, Fumin Shen, Heng Tao Shen

    IEEE TNNLS'2021 [Paper]

    MetaMixup Framework

  • LUMix: Improving Mixup by Better Modelling Label Uncertainty

    Shuyang Sun, Jie-Neng Chen, Ruifei He, Alan Yuille, Philip Torr, Song Bai

    ICASSP'2024 [Paper]
    [Code]

    LUMix Framework

  • SUMix: Mixup with Semantic and Uncertain Information

    Huafeng Qin, Xin Jin, Hongyu Zhu, Hongchao Liao, Mounîm A. El-Yacoubi, Xinbo Gao

    ECCV'2024 [Paper]
    [Code]

    SUMix Framework

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Generating Label

  • GenLabel: Mixup Relabeling using Generative Models

    Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee

    ICML'2022 [Paper]
    GenLabel Framework

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Attention Score

  • All Tokens Matter: Token Labeling for Training Better Vision Transformers

    Zihang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, Jiashi Feng

    NIPS'2021 [Paper]
    [Code]

    Token Labeling Framework

  • TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers

    Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, Yu Liu

    ECCV'2022 [Paper]
    [Code]

    TokenMix Framework

  • TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers

    Hyeong Kyu Choi, Joonmyung Choi, Hyunwoo J. Kim

    NIPS'2022 [Paper]
    [Code]

    TokenMixup Framework

  • MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer

    Qihao Zhao, Yangyu Huang, Wei Hu, Fan Zhang, Jun Liu

    ICLR'2023 [Paper]
    [Code]

    MixPro Framework

  • Token-Label Alignment for Vision Transformers

    Han Xiao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu

    ICCV'2023 [Paper]
    [Code]

    TL-Align Framework

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Saliency Token

  • SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data

    Shaoli Huang, Xinchao Wang, Dacheng Tao

    AAAI'2021 [Paper]
    [Code]

    SnapMix Framework

  • Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing

    Joonhyung Park, June Yong Yang, Jinwoo Shin, Sung Ju Hwang, Eunho Yang

    AAAI'2022 [Paper]

    Saliency Grafting Framework

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Self-Supervised Learning

Contrastive Learning

  • MixCo: Mix-up Contrastive Learning for Visual Representation

    Sungnyun Kim, Gihun Lee, Sangmin Bae, Se-Young Yun

    NIPSW'2020 [Paper]
    [Code]

    MixCo Framework

  • Hard Negative Mixing for Contrastive Learning

    Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, Diane Larlus

    NIPS'2020 [Paper]
    [Code]

    MoCHi Framework

  • i-Mix A Domain-Agnostic Strategy for Contrastive Representation Learning

    Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee

    ICLR'2021 [Paper]
    [Code]

    i-Mix Framework

  • Beyond Single Instance Multi-view Unsupervised Representation Learning

    Xiangxiang Chu, Xiaohang Zhan, Xiaolin Wei

    BMVC'2022 [Paper]

    BSIM Framework

  • Improving Contrastive Learning by Visualizing Feature Transformation

    Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, Chang Wen Chen

    ICCV'2021 [Paper]
    [Code]

    FT Framework

  • Mix-up Self-Supervised Learning for Contrast-agnostic Applications

    Yichen Zhang, Yifang Yin, Ying Zhang, Roger Zimmermann

    ICME'2021 [Paper]

    MixSSL Framework

  • Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning

    Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng

    NIPS'2021 [Paper]
    [Code]

    Co-Tuning Framework

  • Center-wise Local Image Mixture For Contrastive Representation Learning

    Hao Li, Xiaopeng Zhang, Hongkai Xiong

    BMVC'2021 [Paper]

    CLIM Framework

  • Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning

    Jingwei Liu, Yi Gu, Shentong Mo, Zhun Sun, Shumin Han, Jiafeng Guo, Xueqi Cheng

    OpenReview'2021 [Paper]

    PCEA Framework

  • Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup

    Siyuan Li, Zicheng Liu, Di Wu, Zihan Liu, Stan Z. Li

    arXiv'2021 [Paper]
    [Code]

    SAMix Framework

  • MixSiam: A Mixture-based Approach to Self-supervised Representation Learning

    Xiaoyang Guo, Tianhao Zhao, Yutian Lin, Bo Du

    OpenReview'2021 [Paper]

    MixSiam Framework

  • Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing

    Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das

    NIPS'2021 [Paper]
    [Code]

    CoMix Framework

  • Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation

    Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, Eric Xing

    AAAI'2022 [Paper]
    [Code]

    Un-Mix Framework

  • m-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning

    Shaofeng Zhang, Meng Liu, Junchi Yan, Hengrui Zhang, Lingxiao Huang, Pinyan Lu, Xiaokang Yang

    KDD'2022 [Paper]
    [Code]

    m-Mix Framework

  • A Simple Data Mixing Prior for Improving Self-Supervised Learning

    Sucheng Ren, Huiyu Wang, Zhengqi Gao, Shengfeng He, Alan Yuille, Yuyin Zhou, Cihang Xie

    CVPR'2022 [Paper]
    [Code]

    SDMP Framework

  • CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping

    Junlin Han, Lars Petersson, Hongdong Li, Ian Reid

    arXiv'2022 [Paper]
    [Code]

    CropMix Framework

  • Mixing up contrastive learning: Self-supervised representation learning for time series

    Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen

    PR Letter'2022 [Paper]

    MCL Framework

  • Towards Domain-Agnostic Contrastive Learning

    Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le

    ICML'2021 [Paper]

    DACL Framework

  • ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning

    Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z.Li

    ICML'2022 [Paper]
    [Code]

    ProGCL Framework

  • Evolving Image Compositions for Feature Representation Learning

    Paola Cascante-Bonilla, Arshdeep Sekhon, Yanjun Qi, Vicente Ordonez

    BMVC'2021 [Paper]

    PatchMix Framework

  • On the Importance of Asymmetry for Siamese Representation Learning

    Xiao Wang, Haoqi Fan, Yuandong Tian, Daisuke Kihara, Xinlei Chen

    CVPR'2022 [Paper]
    [Code]

    ScaleMix Framework

  • Geodesic Multi-Modal Mixup for Robust Fine-Tuning

    Changdae Oh, Junhyuk So, Hoyoon Byun, YongTaek Lim, Minchul Shin, Jong-June Jeon, Kyungwoo Song

    NIPS'2023 [Paper]
    [Code]

    m2-Mix Framework

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Masked Image Modeling

  • i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable

    Kevin Zhang, Zhiqiang Shen

    arXiv'2022 [Paper]
    [Code]

    i-MAE Framework

  • MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers

    Jihao Liu, Xin Huang, Jinliang Zheng, Yu Liu, Hongsheng Li

    CVPR'2023 [Paper]
    [Code]

    MixMAE Framework

  • Mixed Autoencoder for Self-supervised Visual Representation Learning

    Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung

    CVPR'2023 [Paper]

    MixedAE Framework

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Semi-Supervised Learning

  • MixMatch: A Holistic Approach to Semi-Supervised Learning

    David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel

    NIPS'2019 [Paper]
    [Code]

    MixMatch Framework

  • ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring

    David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel

    ICLR'2020 [Paper]
    [Code]

    ReMixMatch Framework

  • DivideMix: Learning with Noisy Labels as Semi-supervised Learning

    Junnan Li, Richard Socher, Steven C.H. Hoi

    ICLR'2020 [Paper]
    [Code]

    DivideMix Framework

  • MixPUL: Consistency-based Augmentation for Positive and Unlabeled Learning

    Tong Wei, Feng Shi, Hai Wang, Wei-Wei Tu. Yu-Feng Li

    arXiv'2020 [Paper]

    MixPUL Framework

  • Milking CowMask for Semi-Supervised Image Classification

    Geoff French, Avital Oliver, Tim Salimans

    NIPS'2020 [Paper]
    [Code]

    CowMask Framework

  • Epsilon Consistent Mixup: Structural Regularization with an Adaptive Consistency-Interpolation Tradeoff

    Vincent Pisztora, Yanglan Ou, Xiaolei Huang, Francesca Chiaromonte, Jia Li

    arXiv'2021 [Paper]

    Epsilon Consistent Mixup (ϵmu) Framework

  • Who Is Your Right Mixup Partner in Positive and Unlabeled Learning

    Changchun Li, Ximing Li, Lei Feng, Jihong Ouyang

    ICLR'2021 [Paper]

    P3Mix Framework

  • Interpolation Consistency Training for Semi-Supervised Learning

    Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz

    NN'2022 [Paper]

    ICT Framework

  • Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation

    Yuanbin Chen, Tao Wang, Hui Tang, Longxuan Zhao, Ruige Zong, Tao Tan, Xinlin Zhang, Tong Tong

    arXiv'2023 [Paper]

    DCPA Framework

  • MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection

    JongMok Kim, Jooyoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak

    CVPR'2022 [Paper]
    [Code]

    MUM Framework

  • Harnessing Hard Mixed Samples with Decoupled Regularizer

    Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li

    NIPS'2023 [Paper]
    [Code]

    DFixMatch Framework

  • Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise

    Fahimeh Fooladgar, Minh Nguyen Nhat To, Parvin Mousavi, Purang Abolmaesumi

    arXiv'2023 [Paper]
    [Code]

    MixEMatch Framework

  • LaserMix for Semi-Supervised LiDAR Semantic Segmentation

    Lingdong Kong, Jiawei Ren, Liang Pan, Ziwei Liu

    CVPR'2023 [Paper]
    [Code] [project]

    LaserMix Framework

  • PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation

    Yu Lei, Haolun Luo, Lituan Wang, Zhenwei Zhang, Lei Zhang

    arXiv'2024 [Paper]
    [Code]

    PCLMix Framework

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CV Downstream Tasks

Regression

  • RegMix: Data Mixing Augmentation for Regression

    Seong-Hyeon Hwang, Steven Euijong Whang

    arXiv'2021 [Paper]

    MixRL Framework

  • C-Mixup: Improving Generalization in Regression

    Huaxiu Yao, Yiping Wang, Linjun Zhang, James Zou, Chelsea Finn

    NIPS'2022 [Paper]
    [Code]

    C-Mixup Framework

  • ExtraMix: Extrapolatable Data Augmentation for Regression using Generative Models

    Kisoo Kwon, Kuhwan Jeong, Sanghyun Park, Sangha Park, Hoshik Lee, Seung-Yeon Kwak, Sungmin Kim, Kyunghyun Cho

    OpenReview'2022 [Paper]

    SupReMix Framework

  • Rank-N-Contrast: Learning Continuous Representations for Regression

    Kaiwen Zha, Peng Cao, Jeany Son, Yuzhe Yang, Dina Katabi

    NIPS'2023 [Paper]
    [Code]

  • Anchor Data Augmentation

    Nora Schneider, Shirin Goshtasbpour, Fernando Perez-Cruz

    NIPS'2023 [Paper]

  • Mixup Your Own Pairs

    Yilei Wu, Zijian Dong, Chongyao Chen, Wangchunshu Zhou, Juan Helen Zhou

    arXiv'2023 [Paper]
    [Code]

    SupReMix Framework

  • Tailoring Mixup to Data using Kernel Warping functions

    Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc

    arXiv'2023 [Paper]
    [Code]

    Warped Mixup Framework

  • OmniMixup: Generalize Mixup with Mixing-Pair Sampling Distribution

    Anonymous

    Openreview'2023 [Paper]

  • Augment on Manifold: Mixup Regularization with UMAP

    Yousef El-Laham, Elizabeth Fons, Dillon Daudert, Svitlana Vyetrenko

    ICASSP'2024 [Paper]

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Long tail distribution

  • Remix: Rebalanced Mixup

    Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan, Wei Wei, Da-Cheng Juan

    ECCVW'2020 [Paper]

    Remix Framework

  • Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective

    Zhengzhuo Xu, Zenghao Chai, Chun Yuan

    NIPS'2021 [Paper]
    [Code]

    UniMix Framework

  • Label-Occurrence-Balanced Mixup for Long-tailed Recognition

    Shaoyu Zhang, Chen Chen, Xiujuan Zhang, Silong Peng

    ICASSP'2022 [Paper]

    OBMix Framework

  • DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition

    Jae Soon Baik, In Young Yoon, Jun Won Choi

    PR'2024 [Paper]

    DBN-Mix Framework

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Segmentation

  • ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning

    Viktor Olsson, Wilhelm Tranheden, Juliano Pinto, Lennart Svensson

    WACV'2021 [Paper]
    [Code]

    ClassMix Framework

  • ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation

    Matheus Barros Pereira, Jefersson Alex dos Santos

    SIBGRAPI'2021 [Paper]

    ChessMix Framework

  • CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision

    Ke Zhang, Xiahai Zhuang

    CVPR'2022 [Paper]
    [Code]

    CyclesMix Framework

  • InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation

    Yi Lin, Zeyu Wang, Kwang-Ting Cheng, Hao Chen

    MICCAI'2022 [Paper]
    [Code]

    InsMix Framework

  • LaserMix for Semi-Supervised LiDAR Semantic Segmentation

    Lingdong Kong, Jiawei Ren, Liang Pan, Ziwei Liu

    CVPR'2023 [Paper]
    [Code] [project]

    LaserMix Framework

  • Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation

    Yuanbin Chen, Tao Wang, Hui Tang, Longxuan Zhao, Ruige Zong, Tao Tan, Xinlin Zhang, Tong Tong

    arXiv'2023 [Paper]

    DCPA Framework

  • SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images

    Jie Feng, Hao Huang, Junpeng Zhang, Weisheng Dong, Dingwen Zhang, Licheng Jiao

    arXiv'2024 [Paper]
    [Code]

    SA-MixNet Framework

  • Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

    Qinghe Ma, Jian Zhang, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao

    CVPR'2024 [Paper]
    [Code]

    MiDSS Framework

  • UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather

    Haimei Zhao, Jing Zhang, Zhuo Chen, Shanshan Zhao, Dacheng Tao

    CVPR'2024 [Paper]
    [Code]

  • ModelMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision

    Ke Zhang, Vishal M. Patel

    MICCAI'2024 [Paper]

    ModelMix Framework

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Object Detection

  • MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection

    JongMok Kim, Jooyoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak

    CVPR'2022 [Paper]
    [Code]

    MUM Framework

  • Mixed Pseudo Labels for Semi-Supervised Object Detection

    Zeming Chen, Wenwei Zhang, Xinjiang Wang, Kai Chen, Zhi Wang

    arXiv'2023 [Paper]
    [Code]

    MixPL Framework

  • MS-DETR: Efficient DETR Training with Mixed Supervision

    Chuyang Zhao, Yifan Sun, Wenhao Wang, Qiang Chen, Errui Ding, Yi Yang, Jingdong Wang

    arXiv'2024 [Paper]
    [Code]

    MS-DETR Framework

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Other Applications

Training Paradigms

Federated Learning

  • XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning

    MyungJae Shin, Chihoon Hwang, Joongheon Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim

    ICML'2020 [Paper]
    [Code]

  • FedMix: Approximation of Mixup Under Mean augmented Federated Learning

    Tehrim Yoon, Sumin Shin, Sung Ju Hwang, Eunho Yang

    ECCV'2022 [Paper]
    [Code]

  • Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup

    Seungeun Oh, Jihong Park, Eunjeong Jeong, Hyesung Kim, Mehdi Bennis, Seong-Lyun Kim

    IEEE Communications Letters'2020 [Paper]

  • StatMix: Data augmentation method that relies on image statistics in federated learning

    Dominik Lewy, Jacek Mańdziuk, Maria Ganzha, Marcin Paprzycki

    ICONIP'2022 [Paper]

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Adversarial Attack and Adversarial Training

  • Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training

    Alfred Laugros, Alice Caplier, Matthieu Ospici

    ECCV'2020 [Paper]

  • Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks

    Tianyu Pang, Kun Xu, Jun Zhu

    ICLR'2020 [Paper]
    [Code]

  • Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization

    Saehyung Lee, Hyungyu Lee, Sungroh Yoon

    CVPR'2020 [Paper]
    [Code]

  • Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup

    Guang Liu, Yuzhao Mao, Hailong Huang, Weiguo Gao, Xuan Li

    EMNLP'2021 [Paper]

  • Adversarially Optimized Mixup for Robust Classification

    Jason Bunk, Srinjoy Chattopadhyay, B. S. Manjunath, Shivkumar Chandrasekaran

    arXiv'2021 [Paper]

  • Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning

    Guillaume P. Archambault, Yongyi Mao, Hongyu Guo, Richong Zhang

    ACL'2021 [Paper]

  • Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy

    Alex Lamb, Vikas Verma, Kenji Kawaguchi, Alexander Matyasko, Savya Khosla, Juho Kannala, Yoshua Bengio

    NN'2021 [Paper]

  • Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction

    Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu

    ICCV'2023 [Paper]

  • Mixup as directional adversarial training

    Guillaume P. Archambault, Yongyi Mao, Hongyu Guo, Richong Zhang

    NIPS'2019 [Paper]
    [Code]

  • On the benefits of defining vicinal distributions in latent space

    Puneet Mangla, Vedant Singh, Shreyas Jayant Havaldar, Vineeth N Balasubramanian

    CVPRW'2021 [Paper]

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Domain Adaption

  • Mix-up Self-Supervised Learning for Contrast-agnostic Applications

    Yichen Zhang, Yifang Yin, Ying Zhang, Roger Zimmermann

    ICDE'2022 [Paper]

  • Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing

    Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das

    NIPS'2021 [Paper]
    [Code]

  • Virtual Mixup Training for Unsupervised Domain Adaptation

    Xudong Mao, Yun Ma, Zhenguo Yang, Yangbin Chen, Qing Li

    arXiv'2019 [Paper]
    [Code]

  • Improve Unsupervised Domain Adaptation with Mixup Training

    Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, Liu Ren

    arXiv'2020 [Paper]

  • Adversarial Domain Adaptation with Domain Mixup

    Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, Wenjun Zhang

    AAAI'2020 [Paper]
    [Code]

  • Dual Mixup Regularized Learning for Adversarial Domain Adaptation

    Yuan Wu, Diana Inkpen, Ahmed El-Roby

    ECCV'2020 [Paper]
    [Code]

  • Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation

    Aadarsh Sahoo, Rameswar Panda, Rogerio Feris, Kate Saenko, Abir Das

    WACV'2023 [Paper]
    [Code]

  • Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation

    Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan

    MICCAI'2023 [Paper]
    [Code]

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Knowledge Distillation

  • MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps

    Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li

    NIPS'2021 [Paper]

  • MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition

    Chuanguang Yang, Zhulin An, Helong Zhou, Linhang Cai, Xiang Zhi, Jiwen Wu, Yongjun Xu, Qian Zhang

    ECCV'2022 [Paper]

  • Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study

    Chuanguang Yang, Zhulin An, Helong Zhou, Linhang Cai, Xiang Zhi, Jiwen Wu, Yongjun Xu, Qian Zhang

    WACV'2023 [Paper]

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Multi-Modal

  • MixGen: A New Multi-Modal Data Augmentation

    Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, Mu Li

    arXiv'2023 [Paper]
    [Code]

  • VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix

    Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo

    ICML'2022 [Paper]

    VLMixer Framework

  • Geodesic Multi-Modal Mixup for Robust Fine-Tuning

    Changdae Oh, Junhyuk So, Hoyoon Byun, YongTaek Lim, Minchul Shin, Jong-June Jeon, Kyungwoo Song

    NIPS'2023 [Paper]
    [Code]

  • PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis

    Efthymios Georgiou, Yannis Avrithis, Alexandros Potamianos

    arXiv'2023 [Paper]

    PowMix Framework

  • Enhance image classification via inter-class image mixup with diffusion model

    Efthymios Georgiou, Yannis Avrithis, Alexandros Potamianos

    CVPR'2024 [Paper]
    [Code]

  • Frequency-Enhanced Data Augmentation for Vision-and-Language Navigation

    Keji He, Chenyang Si, Zhihe Lu, Yan Huang, Liang Wang, Xinchao Wang

    NIPS'2023 [Paper]
    [Code]

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Beyond Vision

NLP

  • Augmenting Data with Mixup for Sentence Classification: An Empirical Study

    Hongyu Guo, Yongyi Mao, Richong Zhang

    arXiv'2019 [Paper]
    [Code]

  • Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup

    Guang Liu, Yuzhao Mao, Hailong Huang, Weiguo Gao, Xuan Li

    EMNLP'2021 [Paper]

  • SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup

    Hongyu Guo, Yongyi Mao, Richong Zhang

    EMNLP'2020 [Paper]
    [Code]

  • Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks

    Lichao Sun, Congying Xia, Wenpeng Yin, Tingting Liang, Philip S. Yu, Lifang He

    COLING'2020 [Paper]

  • Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data

    Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, Chao Zhang

    EMNLP'2020 [Paper]
    [Code]

  • Augmenting NLP Models using Latent Feature Interpolations

    Amit Jindal, Arijit Ghosh Chowdhury, Aniket Didolkar, Di Jin, Ramit Sawhney, Rajiv Ratn Shah

    COLING'2020 [Paper]

  • MixText: Linguistically-informed Interpolation of Hidden Space for Semi-Supervised Text Classification

    Jiaao Chen, Zichao Yang, Diyi Yang

    ACL'2020 [Paper]
    [Code]

  • Sequence-Level Mixed Sample Data Augmentation

    Jiaao Chen, Zichao Yang, Diyi Yang

    EMNLP'2020 [Paper]
    [Code]

  • AdvAug: Robust Adversarial Augmentation for Neural Machine Translation

    Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein

    ACL'2020 [Paper]
    [Code]

  • Local Additivity Based Data Augmentation for Semi-supervised NER

    Jiaao Chen, Zhenghui Wang, Ran Tian, Zichao Yang, Diyi Yang

    ACL'2020 [Paper]
    [Code]

  • Mixup Decoding for Diverse Machine Translation

    Jicheng Li, Pengzhi Gao, Xuanfu Wu, Yang Feng, Zhongjun He, Hua Wu, Haifeng Wang

    EMNLP'2021 [Paper]

  • TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding

    Le Zhang, Zichao Yang, Diyi Yang

    NAALC'2022 [Paper]
    [Code]

  • STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation

    Qingkai Fang, Rong Ye, Lei Li, Yang Feng, Mingxuan Wang

    ACL'2022 [Paper]
    [Code]

  • AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation

    Chang Jin, Shigui Qiu, Nini Xiao, Hao Jia

    IJCAI'2022 [Paper]

  • Enhancing Cross-lingual Transfer by Manifold Mixup

    Huiyun Yang, Huadong Chen, Hao Zhou, Lei Li

    ICLR'2022 [Paper]
    [Code]

  • Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation

    Yong Cheng, Ankur Bapna, Orhan Firat, Yuan Cao, Pidong Wang, Wolfgang Macherey

    ACL'2022 [Paper]

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GNN

  • Node Augmentation Methods for Graph Neural Network based Object Classification

    Yifan Xue; Yixuan Liao; Xiaoxin Chen; Jingwei Zhao

    CDS'2021 [Paper]

  • Mixup for Node and Graph Classification

    Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi

    WWW'2021 [Paper]
    [Code]

  • Graph Mixed Random Network Based on PageRank

    Qianli Ma, Zheng Fan, Chenzhi Wang, Hongye Tan

    Symmetry'2022 [Paper]

  • GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

    Tianxiang Zhao, Xiang Zhang, Suhang Wang

    WSDM'2021 [Paper]

  • GraphMix: Improved Training of GNNs for Semi-Supervised Learning

    Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang

    AAAI'2021 [Paper]
    [Code]

  • GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction

    Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan.Z.Li

    ECML-PKDD'2022 [Paper]
    [Code]

  • Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation

    Joonhyung Park, Hajin Shim, Eunho Yang

    AAAI'2022 [Paper]
    [Code]

  • G-Mixup: Graph Data Augmentation for Graph Classification

    Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu

    ICML'2022 [Paper]

  • Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications

    Xinyu Ma, Xu Chu, Yasha Wang, Yang Lin, Junfeng Zhao, Liantao Ma, Wenwu Zhu

    NIPS'2023 [Paper]
    [code]

  • iGraphMix: Input Graph Mixup Method for Node Classification

    Jongwon Jeong, Hoyeop Lee, Hyui Geon Yoon, Beomyoung Lee, Junhee Heo, Geonsoo Kim, Kim Jin Seon

    ICLR'2024 [Paper]

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3D Point

  • PointMixup: Augmentation for Point Clouds

    Yunlu Chen, Vincent Tao Hu, Efstratios Gavves, Thomas Mensink, Pascal Mettes, Pengwan Yang, Cees G.M. Snoek

    ECCV'2020 [Paper]
    [Code]

  • PointCutMix: Regularization Strategy for Point Cloud Classification

    Jinlai Zhang, Lyujie Chen, Bo Ouyang, Binbin Liu, Jihong Zhu, Yujing Chen, Yanmei Meng, Danfeng Wu

    Neurocomputing'2022 [Paper]
    [Code]

  • Regularization Strategy for Point Cloud via Rigidly Mixed Sample

    Dogyoon Lee, Jaeha Lee, Junhyeop Lee, Hyeongmin Lee, Minhyeok Lee, Sungmin Woo, Sangyoun Lee

    CVPR'2021 [Paper]
    [Code]

  • Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

    Jaeseok Choi, Yeji Song, Nojun Kwak

    IROS'2021 [Paper]
    [Code]

  • Point MixSwap: Attentional Point Cloud Mixing via Swapping Matched Structural Divisions

    Ardian Umam, Cheng-Kun Yang, Yung-Yu Chuang, Jen-Hui Chuang, Yen-Yu Lin

    ECCV'2022 [Paper]
    [Code]

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Other

  • Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning

    Byungsoo Ko, Geonmo Gu

    CVPR'2020 [Paper]
    [Code]

  • SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map

    Jaehyeop Choi, Chaehyeon Lee, Donggyu Lee, Heechul Jung

    Sensor'2021 [Paper]

  • Octave Mix: Data Augmentation Using Frequency Decomposition for Activity Recognition

    Tatsuhito Hasegawa

    IEEE Access'2021 [Paper]

  • Guided Interpolation for Adversarial Training

    Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama

    arXiv'2021 [Paper]

  • Recall@k Surrogate Loss with Large Batches and Similarity Mixup

    Yash Patel, Giorgos Tolias, Jiri Matas

    CVPR'2022 [Paper]
    [Code]

  • Contrastive-mixup Learning for Improved Speaker Verification

    Xin Zhang, Minho Jin, Roger Cheng, Ruirui Li, Eunjung Han, Andreas Stolcke

    ICASSP'2022 [Paper]

  • Noisy Feature Mixup

    Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney

    ICLR'2022 [Paper]
    [Code]

  • It Takes Two to Tango: Mixup for Deep Metric Learning

    Shashanka Venkataramanan, Bill Psomas, Ewa Kijak, Laurent Amsaleg, Konstantinos Karantzalos, Yannis Avrithis

    ICLR'2022 [Paper]
    [Code]

  • Representational Continuity for Unsupervised Continual Learning

    Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, Sung Ju Hwang

    ICLR'2022 [Paper]
    [Code]

  • Expeditious Saliency-guided Mix-up through Random Gradient Thresholding

    Remy Sun, Clement Masson, Gilles Henaff, Nicolas Thome, Matthieu Cord.

    ICPR'2022 [Paper]

  • Guarding Barlow Twins Against Overfitting with Mixed Samples

    Wele Gedara Chaminda Bandara, Celso M. De Melo, Vishal M. Patel

    arXiv'2023 [Paper]
    [Code]

  • Infinite Class Mixup

    Thomas Mensink, Pascal Mettes

    arXiv'2023 [Paper]

  • Semantic Equivariant Mixup

    Zongbo Han, Tianchi Xie, Bingzhe Wu, Qinghua Hu, Changqing Zhang

    arXiv'2023 [Paper]

  • G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima

    Xingyu Li, Bo Tang

    arXiv'2023 [Paper]

  • Inter-Instance Similarity Modeling for Contrastive Learning

    Chengchao Shen, Dawei Liu, Hao Tang, Zhe Qu, Jianxin Wang

    arXiv'2023 [Paper]
    [Code]

  • Single-channel speech enhancement using learnable loss mixup

    Oscar Chang, Dung N. Tran, Kazuhito Koishida

    arXiv'2023 [Paper]

  • Selective Volume Mixup for Video Action Recognition

    Yi Tan, Zhaofan Qiu, Yanbin Hao, Ting Yao, Xiangnan He, Tao Mei

    arXiv'2023 [Paper]

  • Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy

    Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn

    CVPR'2020 & IJCV'2024 [Paper]
    [Code]

  • DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models

    Zhihan Zhou, Weimin Wu, Harrison Ho, Jiayi Wang, Lizhen Shi, Ramana V Davuluri, Zhong Wang, Han Liu

    arXiv'2024 [Paper]
    [Code]

  • ContextMix: A context-aware data augmentation method for industrial visual inspection systems

    Hyungmin Kim, Donghun Kim, Pyunghwan Ahn, Sungho Suh, Hansang Cho, Junmo Kim

    EAAI'2024 [Paper]

  • Robust Image Denoising through Adversarial Frequency Mixup

    Donghun Ryou, Inju Ha, Hyewon Yoo, Dongwan Kim, Bohyung Han

    CVPR'2024 [Paper]
    [Code]

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Analysis and Theorem

  • Understanding Mixup Training Methods

    Daojun Liang, Feng Yang, Tian Zhang, Peter Yang

    NIPS'2019 [Paper]

  • MixUp as Locally Linear Out-Of-Manifold Regularization

    Hongyu Guo, Yongyi Mao, Richong Zhang

    AAAI'2019 [Paper]

  • MixUp as Directional Adversarial Training

    Chanwoo Park, Sangdoo Yun, Sanghyuk Chun

    NIPS'2019 [Paper]

  • On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks

    Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, Sarah Michalak

    NIPS'2019 [Paper]
    [Code]

  • On Mixup Regularization

    Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert

    arXiv'2020 [Paper]

  • Mixup Training as the Complexity Reduction

    Masanari Kimura

    arXiv'2021 [Paper]

  • How Does Mixup Help With Robustness and Generalization

    Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou

    ICLR'2021 [Paper]

  • Mixup Without Hesitation

    Hao Yu, Huanyu Wang, Jianxin Wu

    ICIG'2022 [Paper]
    [Code]

  • RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness

    Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip H.S. Torr, Puneet K. Dokania

    NIPS'2022 [Paper]
    [Code]

  • A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective

    Chanwoo Park, Sangdoo Yun, Sanghyuk Chun

    NIPS'2022 [Paper]
    [Code]

  • Towards Understanding the Data Dependency of Mixup-style Training

    Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge

    ICLR'2022 [Paper]
    [Code]

  • When and How Mixup Improves Calibration

    Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou

    ICML'2022 [Paper]

  • Provable Benefit of Mixup for Finding Optimal Decision Boundaries

    Junsoo Oh, Chulhee Yun

    ICML'2023 [Paper]

  • On the Pitfall of Mixup for Uncertainty Calibration

    Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang

    CVPR'2023 [Paper]

  • Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study

    Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan Turaga

    WACV'2023 [Paper]
    [Code]

  • Over-Training with Mixup May Hurt Generalization

    Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao

    ICLR'2023 [Paper]

  • Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability

    Soyoun Won, Sung-Ho Bae, Seong Tae Kim

    arXiv'2023 [Paper]

  • Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup

    Damien Teney, Jindong Wang, Ehsan Abbasnejad

    ICML'2024 [Paper]

  • Pushing Boundaries: Mixup's Influence on Neural Collapse

    Quinn Fisher, Haoming Meng, Vardan Papyan

    ICLR'2024 [Paper]

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Survey

  • A survey on Image Data Augmentation for Deep Learning

    Connor Shorten and Taghi Khoshgoftaar

    Journal of Big Data'2019 [Paper]

  • An overview of mixing augmentation methods and augmentation strategies

    Dominik Lewy and Jacek Ma ́ndziuk

    Artificial Intelligence Review'2022 [Paper]

  • Image Data Augmentation for Deep Learning: A Survey

    Suorong Yang, Weikang Xiao, Mengcheng Zhang, Suhan Guo, Jian Zhao, Furao Shen

    arXiv'2022 [Paper]

  • A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability

    Chengtai Cao, Fan Zhou, Yurou Dai, Jianping Wang

    arXiv'2022 [Paper]
    [Code]

  • A Survey of Automated Data Augmentation for Image Classification: Learning to Compose, Mix, and Generate

    Tsz-Him Cheung, Dit-Yan Yeung

    IEEE TNNLS'2023 [Paper]

  • Survey: Image Mixing and Deleting for Data Augmentation

    Humza Naveed, Saeed Anwar, Munawar Hayat, Kashif Javed, Ajmal Mian

    EAAI'2024 [Paper]

  • A Survey on Mixup Augmentations and Beyond

    Xin Jin, Hongyu Zhu, Siyuan Li, Zedong Wang, Zecheng Liu, Chang Yu, Huafeng Qin, Stan. Z. Li

    arXiv'2024 [Paper]

Benchmark

  • OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification

    Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Weiyang Jin, Stan Z. Li

    arXiv'2024 [Paper]
    [Code]

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Classification Results on Datasets

Mixup methods classification results on general datasets: CIFAR10 \ CIFAR100, TinyImageNet, and ImageNet-1K. $(\cdot)$ denotes training epochs based on ResNet18 (R18), ResNet50 (R50), ResNeXt50 (RX50), PreActResNet18 (PreActR18), and Wide-ResNet28 (WRN28-10, WRN28-8).

Method Publish CIFAR10 CIFAR100 CIFAR100 CIFAR100 CIFAR100 CIFAR100 Tiny-ImageNet Tiny-ImageNet ImageNet-1K ImageNet-1K
R18 R18 RX50 PreActR18 WRN28-10 WRN28-8 R18 RX50 R18 R50
MixUp ICLR'2018 96.62(800) 79.12(800) 82.10(800) 78.90(200) 82.50(200) 82.82(400) 63.86(400) 66.36(400) 69.98(100) 77.12(100)
CutMix ICCV'2019 96.68(800) 78.17(800) 78.32(800) 76.80(1200) 83.40(200) 84.45(400) 65.53(400) 66.47(400) 68.95(100) 77.17(100)
Manifold Mixup ICML'2019 96.71(800) 80.35(800) 82.88(800) 79.66(1200) 81.96(1200) 83.24(400) 64.15(400) 67.30(400) 69.98(100) 77.01(100)
FMix arXiv'2020 96.18(800) 79.69(800) 79.02(800) 79.85(200) 82.03(200) 84.21(400) 63.47(400) 65.08(400) 69.96(100) 77.19(100)
SmoothMix CVPRW'2020 96.17(800) 78.69(800) 78.95(800) - - 82.09(400) - - - 77.66(300)
GridMix PR'2020 96.56(800) 78.72(800) 78.90(800) - - 84.24(400) 64.79(400) - - -
ResizeMix arXiv'2020 96.76(800) 80.01(800) 80.35(800) - 85.23(200) 84.87(400) 63.47(400) 65.87(400) 69.50(100) 77.42(100)
SaliencyMix ICLR'2021 96.20(800) 79.12(800) 78.77(800) 80.31(300) 83.44(200) 84.35(400) 64.60(400) 66.55(400) 69.16(100) 77.14(100)
Attentive-CutMix ICASSP'2020 96.63(800)n 78.91(800) 80.54(800) - - 84.34(400) 64.01(400) 66.84(400) - 77.46(100)
Saliency Grafting AAAI'2022 - 80.83(800) 83.10(800) - 84.68(300) - 64.84(600) 67.83(400) - 77.65(100)
PuzzleMix ICML'2020 97.10(800) 81.13(800) 82.85(800) 80.38(1200) 84.05(200) 85.02(400) 65.81(400) 67.83(400) 70.12(100) 77.54(100)
Co-Mix ICLR'2021 97.15(800) 81.17(800) 82.91(800) 80.13(300) - 85.05(400) 65.92(400) 68.02(400) - 77.61(100)
SuperMix CVPR'2021 - - - 79.07(2000) 93.60(600) - - - - 77.60(600)
RecursiveMix NIPS'2022 - 81.36(200) - 80.58(2000) - - - - - 79.20(300)
AutoMix ECCV'2022 97.34(800) 82.04(800) 83.64(800) - - 85.18(400) 67.33(400) 70.72(400) 70.50(100) 77.91(100)
SAMix arXiv'2021 97.50(800) 82.30(800) 84.42(800) - - 85.50(400) 68.89(400) 72.18(400) 70.83(100) 78.06(100)
AlignMixup CVPR'2022 - - - 81.71(2000) - - - - - 78.00(100)
MultiMix NIPS'2023 - - - 81.82(2000) - - - - - 78.81(300)
GuidedMixup AAAI'2023 - - - 81.20(300) 84.02(200) - - - - 77.53(100)
Catch-up Mix AAAI'2023 - 82.10(400) 83.56(400) 82.24(2000) - - 68.84(400) - - 78.71(300)
LGCOAMix TIP'2024 - 82.34(800) 84.11(800) - - - 68.27(400) 73.08(400) - -
AdAutoMix ICLR'2024 97.55(800) 82.32(800) 84.42(800) - - 85.32(400) 69.19(400) 72.89(400) 70.86(100) 78.04(100)

Mixup methods classification results on ImageNet-1K dataset use ViT-based models: DeiT, Swin Transformer (Swin), Pyramid Vision Transformer (PVT), and ConvNext trained 300 epochs.

Method Publish ImageNet-1K ImageNet-1K ImageNet-1K ImageNet-1K ImageNet-1K ImageNet-1K ImageNet-1K
DieT-Tiny DieT-Small DieT-Base Swin-Tiny PVT-Tiny PVT-Small ConvNeXt-Tiny
MixUp ICLR'2018 74.69 77.72 78.98 81.01 75.24 78.69 80.88
CutMix ICCV'2019 74.23 80.13 81.61 81.23 75.53 79.64 81.57
FMix arXiv'2020 74.41 77.37 - 79.60 75.28 78.72 81.04
ResizeMix arXiv'2020 74.79 78.61 80.89 81.36 76.05 79.55 81.64
SaliencyMix ICLR'2021 74.17 79.88 80.72 81.37 75.71 79.69 81.33
Attentive-CutMix ICASSP'2020 74.07 80.32 82.42 81.29 74.98 79.84 81.14
PuzzleMix ICML'2020 73.85 80.45 81.63 81.47 75.48 79.70 81.48
AutoMix ECCV'2022 75.52 80.78 82.18 81.80 76.38 80.64 82.28
SAMix arXiv'2021 75.83 80.94 82.85 81.87 76.60 80.78 82.35
TransMix CVPR'2022 74.56 80.68 82.51 81.80 75.50 80.50 -
TokenMix ECCV'2022 75.31 80.80 82.90 81.60 75.60 - 73.97
TL-Align ICCV'2023 73.20 80.60 82.30 81.40 75.50 80.40 -
SMMix ICCV'2023 75.56 81.10 82.90 81.80 75.60 81.03 -
Mixpro ICLR'2023 73.80 81.30 82.90 82.80 76.70 81.20 -
LUMix ICASSP'2024 - 80.60 80.20 81.70 - - 82.50

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Summary of datasets for mixup methods tasks. Link to dataset websites is provided.

Dataset Type Label Task Total data number Link
MINIST Image 10 Classification 70,000 MINIST
Fashion-MNIST Image 10 Classification 70,000 Fashion-MINIST
CIFAR10 Image 10 Classification 60,000 CIFAR10
CIFAR100 Image 100 Classification 60,000 CIFAR100
SVHN Image 10 Classification 630,420 SVHN
GTSRB Image 43 Classification 51,839 GTSRB
STL10 Image 10 Classification 113,000 STL10
Tiny-ImageNet Image 200 Classification 100,000 Tiny-ImageNet
ImageNet-1K Image 1,000 Classification 1,431,167 ImageNet-1K
CUB-200-2011 Image 200 Classification, Object Detection 11,788 CUB-200-2011
FGVC-Aircraft Image 102 Classification 10,200 FGVC-Aircraft
StanfordCars Image 196 Classification 16,185 StanfordCars
Oxford Flowers Image 102 Classification 8,189 Oxford Flowers
Caltech101 Image 101 Classification 9,000 Caltech101
SOP Image 22,634 Classification 120,053 SOP
Food-101 Image 101 Classification 101,000 Food-101
SUN397 Image 899 Classification 130,519 SUN397
iNaturalist Image 5,089 Classification 675,170 iNaturalist
CIFAR-C Image 10,100 Corruption Classification 60,000 CIFAR-C
CIFAR-LT Image 10,100 Long-tail Classification 60,000 CIFAR-LT
ImageNet-1K-C Image 1,000 Corruption Classification 1,431,167 ImageNet-1K-C
ImageNet-A Image 200 Classification 7,500 ImageNet-A
Pascal VOC 102 Image 20 Object Detection 33,043 Pascal VOC 102
MS-COCO Detection Image 91 Object Detection 164,062 MS-COCO Detection
DSprites Image 737,280*6 Disentanglement 737,280 DSprites
Place205 Image 205 Recognition 2,500,000 Place205
Pascal Context Image 459 Segmentation 10,103 Pascal Context
ADE20K Image 3,169 Segmentation 25,210 ADE20K
Cityscapes Image 19 Segmentation 5,000 Cityscapes
StreetHazards Image 12 Segmentation 7,656 StreetHazards
PACS Image 7*4 Domain Classification 9,991 PACS
BRACS Medical Image 7 Classification 4,539 BRACS
BACH Medical Image 4 Classification 400 BACH
CAME-Lyon16 Medical Image 2 Anomaly Detection 360 CAME-Lyon16
Chest X-Ray Medical Image 2 Anomaly Detection 5,856 Chest X-Ray
BCCD Medical Image 4,888 Object Detection 364 BCCD
TJU600 Palm-Vein Image 600 Classification 12,000 TJU600
VERA220 Palm-Vein Image 220 Classification 2,200 VERA220
CoNLL2003 Text 4 Classification 2,302 CoNLL2003
20 Newsgroups Text 20 OOD Detection 20,000 20 Newsgroups
WOS Text 134 OOD Detection 46,985 WOS
SST-2 Text 2 Sentiment Understanding 68,800 SST-2
Cora Graph 7 Node Classification 2,708 Cora
Citeseer Graph 6 Node Classification 3,312 Citeseer
PubMed Graph 3 Node Classification 19,717 PubMed
BlogCatalog Graph 39 Node Classification 10,312 BlogCatalog
Google Commands Speech 30 Classification 65,000 Google Commands
VoxCeleb2 Speech 6,112 Sound Classification 1,000,000+ VoxCeleb2
VCTK Speech 110 Enhancement 44,000 VCTK
ModelNet40 3D Point Cloud 40 Classification 12,311 ModelNet40
ScanObjectNN 3D Point Cloud 15 Classification 15,000 ScanObjectNN
ShapeNet 3D Point Cloud 16 Recognition, Classification 16,880 ShapeNet
KITTI360 3D Point Cloud 80,256 Detection, Segmentation 14,999 KITTI360
UCF101 Video 101 Action Recognition 13,320 UCF101
Kinetics400 Video 400 Action Recognition 260,000 Kinetics400
Airfoil Tabular - Regression 1,503 Airfoil
NO2 Tabular - Regression 500 NO2
Exchange-Rate Timeseries - Regression 7,409 Exchange-Rate
Electricity Timeseries - Regression 26,113 Electricity

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Contribution

Feel free to send pull requests to add more links with the following Markdown format. Note that the abbreviation, the code link, and the figure link are optional attributes.

* **TITLE**<br>
*AUTHER*<br>
PUBLISH'YEAR [[Paper](link)] [[Code](link)]
   <details close>
   <summary>ABBREVIATION Framework</summary>
   <p align="center"><img width="90%" src="link_to_image" /></p>
   </details>

Citation

If you feel that our work has contributed to your research, please cite it, thanks. 🥰

@article{jin2024survey,
  title={A Survey on Mixup Augmentations and Beyond},
  author={Jin, Xin and Zhu, Hongyu and Li, Siyuan and Wang, Zedong and Liu, Zicheng and Yu, Chang and Qin, Huafeng and Li, Stan Z},
  journal={arXiv preprint arXiv:2409.05202},
  year={2024}
}

Current contributors include: Siyuan Li (@Lupin1998), Xin Jin (@JinXins), Zicheng Liu (@pone7), and Zedong Wang (@Jacky1128). We thank all contributors for Awesome-Mixup!

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License

This project is released under the Apache 2.0 license.

Acknowledgement

This repository is built using the OpenMixup library and Awesome README repository.

Contributors

LUJI
Apache License 2.0
Created September 20, 2022
Updated March 7, 2026